Tensorflow version: 2.1.0
Data Source: S&P500 Daily Prices 1986 - 2018
| date | close | |
|---|---|---|
| 0 | 1986-01-02 | 209.59 |
| 1 | 1986-01-03 | 210.88 |
| 2 | 1986-01-06 | 210.65 |
| 3 | 1986-01-07 | 213.80 |
| 4 | 1986-01-08 | 207.97 |
(8192, 2)
(6553, 2) (1639, 2)
(6523, 30, 1)
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= lstm (LSTM) (None, 128) 66560 _________________________________________________________________ dropout (Dropout) (None, 128) 0 _________________________________________________________________ repeat_vector (RepeatVector) (None, 30, 128) 0 _________________________________________________________________ lstm_1 (LSTM) (None, 30, 128) 131584 _________________________________________________________________ dropout_1 (Dropout) (None, 30, 128) 0 _________________________________________________________________ time_distributed (TimeDistri (None, 30, 1) 129 ================================================================= Total params: 198,273 Trainable params: 198,273 Non-trainable params: 0 _________________________________________________________________
Train on 5870 samples, validate on 653 samples Epoch 1/100 5870/5870 [==============================] - 12s 2ms/sample - loss: 0.1625 - val_loss: 0.1610 Epoch 2/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.1114 - val_loss: 0.0986 Epoch 3/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0903 - val_loss: 0.0443 Epoch 4/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0802 - val_loss: 0.0442 Epoch 5/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0717 - val_loss: 0.0639 Epoch 6/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0776 - val_loss: 0.0327 Epoch 7/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0750 - val_loss: 0.0313 Epoch 8/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0744 - val_loss: 0.0578 Epoch 9/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0758 - val_loss: 0.0522 Epoch 10/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0765 - val_loss: 0.0302 Epoch 11/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0734 - val_loss: 0.0610 Epoch 12/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0749 - val_loss: 0.0629 Epoch 13/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0751 - val_loss: 0.0274 Epoch 14/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0746 - val_loss: 0.0407 Epoch 15/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0741 - val_loss: 0.0454 Epoch 16/100 5870/5870 [==============================] - 7s 1ms/sample - loss: 0.0762 - val_loss: 0.0277
1609/1609 [==============================] - 1s 374us/sample - loss: 0.2685
0.26845344528787396
| date | close | loss | threshold | anomaly | |
|---|---|---|---|---|---|
| 8187 | 2018-06-25 | 4.493228 | 0.638412 | 0.65 | False |
| 8188 | 2018-06-26 | 4.507583 | 0.691408 | 0.65 | True |
| 8189 | 2018-06-27 | 4.451431 | 0.696458 | 0.65 | True |
| 8190 | 2018-06-28 | 4.491406 | 0.727353 | 0.65 | True |
| 8191 | 2018-06-29 | 4.496343 | 0.709381 | 0.65 | True |
| date | close | loss | threshold | anomaly | |
|---|---|---|---|---|---|
| 7474 | 2015-08-25 | 2.457439 | 0.655041 | 0.65 | True |
| 7475 | 2015-08-26 | 2.632149 | 0.711078 | 0.65 | True |
| 8090 | 2018-02-05 | 4.329949 | 0.657327 | 0.65 | True |
| 8091 | 2018-02-06 | 4.440671 | 0.846347 | 0.65 | True |
| 8092 | 2018-02-07 | 4.408365 | 0.822247 | 0.65 | True |
| date | close | loss | threshold | anomaly | |
|---|---|---|---|---|---|
| 8145 | 2018-04-25 | 4.307086 | 0.653256 | 0.65 | True |
| 8188 | 2018-06-26 | 4.507583 | 0.691408 | 0.65 | True |
| 8189 | 2018-06-27 | 4.451431 | 0.696458 | 0.65 | True |
| 8190 | 2018-06-28 | 4.491406 | 0.727353 | 0.65 | True |
| 8191 | 2018-06-29 | 4.496343 | 0.709381 | 0.65 | True |